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本文引用的文献

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Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia.利用可能患有肺炎的专家注释扩充美国国立卫生研究院胸部X光数据集。
Radiol Artif Intell. 2019 Jan 30;1(1):e180041. doi: 10.1148/ryai.2019180041. eCollection 2019 Jan.
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Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions.医学成像中人工智能研究的相关挑战及图像分析竞赛的重要性
Radiol Artif Intell. 2019 Jan 30;1(1):e180031. doi: 10.1148/ryai.2019180031. eCollection 2019 Jan.
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Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.深度学习软件标记的急性颅内出血头部 CT 扫描分析。
Neuroradiology. 2020 Mar;62(3):335-340. doi: 10.1007/s00234-019-02330-w. Epub 2019 Dec 11.
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Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.使用深度学习技术对头 CT 进行急性颅内出血的专家级检测。
Proc Natl Acad Sci U S A. 2019 Nov 5;116(45):22737-22745. doi: 10.1073/pnas.1908021116. Epub 2019 Oct 21.
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Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning.深度学习在神经放射学中的应用:基于迁移学习的脑出血分类。
Comput Intell Neurosci. 2019 Jun 3;2019:4629859. doi: 10.1155/2019/4629859. eCollection 2019.
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Detecting Intracranial Hemorrhage with Deep Learning.利用深度学习检测颅内出血。
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:583-587. doi: 10.1109/EMBC.2018.8512336.
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Intracerebral haemorrhage: current approaches to acute management.脑出血:急性处理的当前方法。
Lancet. 2018 Oct 6;392(10154):1257-1268. doi: 10.1016/S0140-6736(18)31878-6.
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Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.深度学习算法在头部 CT 扫描中关键发现检测的应用:一项回顾性研究。
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通过合作构建机器学习数据集:2019年RSNA脑CT出血挑战赛

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

作者信息

Flanders Adam E, Prevedello Luciano M, Shih George, Halabi Safwan S, Kalpathy-Cramer Jayashree, Ball Robyn, Mongan John T, Stein Anouk, Kitamura Felipe C, Lungren Matthew P, Choudhary Gagandeep, Cala Lesley, Coelho Luiz, Mogensen Monique, Morón Fanny, Miller Elka, Ikuta Ichiro, Zohrabian Vahe, McDonnell Olivia, Lincoln Christie, Shah Lubdha, Joyner David, Agarwal Amit, Lee Ryan K, Nath Jaya

机构信息

Department of Radiology/Division of Neuroradiology, Thomas Jefferson University Hospital, 132 S Tenth St, Suite 1080B Main Building, Philadelphia, PA 19107 (A.E.F.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Radiology, Stanford University, Stanford, Calif (S.S.H.); Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Mass (J.K.); Quantitative Sciences Unit, Stanford University, Stanford, Calif (R.B.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); MD.ai, New York, NY (A.S.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.C.K.); Department of Radiology, Stanford University, Stanford, Calif (M.P.L.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (G.C.); Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia (L. Cala); Advanced Diagnostic Imaging, Clínica DAPI, Curitiba, Brazil (L. Coelho); Department of Radiology, University of Washington, Seattle, Wash (M.M.); Department of Radiology, Baylor College of Medicine, Houston, Tex (F.M., C.L.); Department of Radiology, University of Ottawa, Ottawa, Canada (E.M.); Department of Radiology & Biomedical Imaging, Yale University, New Haven, Conn (I.I., V.Z.); Department of Medical Imaging, Gold Coast University Hospital, Southport, Australia (O.M.); Department of Neuroradiology, University of Utah Health Sciences Center, Salt Lake City, Utah (L.S.); Department of Radiology and Medical Imaging, University of Virginia Health, Charlottesville, Va (D.J.); Division of Neuroradiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.A.); Department of Radiology, Albert Einstein Healthcare Network, Philadelphia, Pa (R.K.L.); and Department of Radiology, SUNY Downstate Medical Center, Albany, NY (J.N.).

出版信息

Radiol Artif Intell. 2020 Apr 29;2(3):e190211. doi: 10.1148/ryai.2020190211. eCollection 2020 May.

DOI:10.1148/ryai.2020190211
PMID:33937827
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8082297/
Abstract

This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT.

摘要

该数据集由脑部CT中常见的五种出血亚型(蛛网膜下腔出血、脑室内出血、硬膜下出血、硬膜外出血和脑实质内出血)的注释组成。